Automatic real-time 2D-to-3D conversion for scenic views

The generation of three-dimensional (3D) videos from monoscopic two-dimensional (2D) videos has received a lot of attention in the last few years. Current conversion techniques are based on generating an estimated depth map for each frame from different depth cues, and then using Depth Image Based Rendering (DIBR) to synthesize the additional views. Efficient interactive techniques have been developed in which multiple depth factors (monocular depth cues) are utilized to estimate the depth map using machine-learning algorithms. The challenge with such methods is that they cannot be used for real-time conversion. We address this problem by proposing an effective scheme that generates high quality depth maps for scenic views in real-time. In our work, we use three depth cues, haze, vertical edges, and sharpness to estimate a sparse depth map. We then obtain the full depth map from the sparse map using an edge-aware interpolation method. Performance evaluations show that our method outperforms the existing state-of-the-art 2D-to3D conversion methods.